Systems and methods for predicting pest pressure using geospatial features and machine learning

Abstract

System and methods for predicting future pest pressures are provided. A pest pressure prediction computing device includes a memory and a processor communicatively coupled to the memory. The processor is programmed to receive trap data for a plurality of pest traps in a geographic location, receive weather data for the geographic location, receive image data for the geographic location, identify at least one geospatial feature within or proximate to the geographic location, apply a machine learning algorithm to the trap data, the weather data, the image data, and the at least one identified geospatial feature to identify a correlation between pest pressure and the at least one geospatial feature, and generate predicted future pest pressures for the geographic location based at least on the identified correlation between pest pressure and the at least one geospatial feature.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to provisional application Ser. No. 62 / 984,885, filed Mar. 4, 2020, which is incorporated herein by reference in its entirety.BACKGROUND[0002]The present application relates generally to a technology that may be used to assist in predicting pest pressure, and more particularly, to network-based systems and methods for predicting pest pressure using geospatial features and machine learning.[0003]Due to the world's increasing population and decreasing amount of arable land, there is a desire for methods and systems to increase the productivity of agricultural crops. At least one factor that impacts the productivity of agricultural crops is pest pressure.[0004]Accordingly, systems and methods have been developed to monitor and analyze pest pressure. For example, in at least some known systems, a plurality of insect traps are placed in a field of interest. To monitor the pest pressure in the field of interest,...

AI Technical Summary

Benefits of technology

The patent describes a device and method for predicting pest pressure in a specific location based on data collected from pest traps, weather data, and image data. The device uses a machine learning algorithm to identify correlations between pest pressure and certain geospatial features, which can then be used to project future pest pressures in the location. Overall, the technology provides a valuable tool for predicting pest pressure and developing effective preventive measures.

Problems solved by technology

However, pest pressure is a relatively complex phenomenon that is governed by several factors.

Thus, accurately predicting future pest pressures based primarily on trap counts may be relatively inaccurate.

Further, at least some known systems for pest pressure monitoring are focused at an individual farm level, resulting in limited visualizations and significant time lag in data collections.

In addition, at least some known systems for predicting future pest pressure rely on static logic (e.g., fixed phenology models and / or decision trees), and are accordingly limited in their ability to accurately predict future pest pressure.

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